Under-reported data analysis with INAR-hidden Markov chains

被引：31

作者：

Fernandez-Fontelo, Amanda ^{[1
]}

Cabana, Alejandra ^{[1
]}

Puig, Pedro ^{[1
]}

Morina, David ^{[2
,3
]}

机构：

[1] Univ Autonoma Barcelona, Dept Matemat, Bellaterra, Spain

[2] Catalan Inst Oncol ICO IDIBELL, Canc Epidemiol Res Program, Unit Infect & Canc UNIC, Barcelona, Spain

[3] Univ Autonoma Barcelona, Fac Med, Unitat Bioestadist, GRAAL, Bellaterra, Spain

来源：

STATISTICS IN MEDICINE | 2016年 / 35卷 / 26期

关键词：

discrete time series; emission probabilities; integer-autoregressive models; thinning operator; under-recorded data; VALUED TIME-SERIES; PERITONEAL MESOTHELIOMA; ILLNESS; MODELS; MORTALITY; OUTBREAK; GERMANY; TRENDS;

D O I：

10.1002/sim.7026

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

In this work, we deal with correlated under-reported data through INAR(1)-hidden Markov chain models. These models are very flexible and can be identified through its autocorrelation function, which has a very simple form. A naive method of parameter estimation is proposed, jointly with the maximum likelihood method based on a revised version of the forward algorithm. The most-probable unobserved time series is reconstructed by means of the Viterbi algorithm. Several examples of application in the field of public health are discussed illustrating the utility of the models. Copyright (C) 2016 John Wiley & Sons, Ltd.

引用

页码：4875 / 4890

页数：16

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